Review of “An overview of methods for incorporating wildfires into forest planning models”
Bibliographic record
Abstract
Literature reviews are always very challenging and the fact that this one is based on a conference presentation inflicts even more constraints on its author. The author reviews the literature pertaining to how fire or the likelihood that part of a forested landscape might burn has been incorporated into forest management planning models. The large number of publications focusing on the problem is testimony to its importance and the author has provided a good overview of the topic, subject of course, to time (presentation) and space (corresponding journal page) constraints. The manuscript itself is reasonably well written and I have no serious concerns, but the author might wish to consider the following comments, the incorporation of some of which might improve the manuscript at some cost in terms of space. I leave it to the author and editor to judge how to balance those two conflicting factors. Page 1 line 6 The term “spatially recognizing” is awkward jargon and I suggest that sentence be re-written. Page 6, para 2, line 11 I realize most people believe “Further, losses from wildfire can destabilize local economies that are dependent on a stable supply of un-burned timber” but I am skeptical that this happens very often. If you want to leave this in I suggest you cite sources to document that. Page 2, para 3 line 1: I understand what you are trying to say but I am not sure the word “sophisticated” is appropriate. Perhaps “complex” or some other more appropriate word? Methodological or Temporal? The author has, for the most part, used a methodological rather than a temporal framework to organize his thoughts. I realize it is difficult to decide how to proceed but if you choose to organize methodologically, I suggest you consider dealing with specific methodologies in the order in which they first
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".